Averaging ensembles are composed of base classifiers which are mainly obtained by modifying various DL elements such as the types of network architectures, and their parameters, data augmentation techniques, and learning meta parameters.
An example is the DeepFace [1]}, where Taigman et al. construct a face verification system of 7 deep networks and achieve 97.35% accuracy, compared to 97.0% obtained using a single face verification network.
In another work, Szegedy et al. [2]} increase the accuracy from 40% (single network) to 43.9% by averaging 6 GoogLeNet networks in the ILSVRC 2015 detection challenge.
Yet another example is the winner of the PlantCLEF2017 competition [3]}, which is formed of 12 networks that are trained with an emphasis on complementarity and achieved a top-1 accuracy of 88.5% in classifying 10,000 different plant species.
Similarly, Gessert et. al. in [4]} employ multi-resolution EfficientNets [5]} for skin lesion classification based on an ensemble of 15 deep networks, where the area under the curve (AUC) is increased from an average of 94 per classifier to \(95.4\) .
